Abstract: In a method for computer driven question identification and understanding within a commercial tender document (CTD), different CTDs from different individuals are uploaded into fixed storage of a computer. Then, different ones of the CTDs are loaded into memory and clustered into different clusters according to at least one clustering criteria. For each one of the CTDs, a corresponding one of the clusters is identified, and a segmentation model selected for the identified one of the clusters. Thereafter, segmentation is performed upon the CTD utilizing the selected segmentation model to produce a set of segmented portions of the CTD. Finally, for each one of the segmented portions, a set of extraction rules mapped to the one of the segmented portions is retrieved, at least one question extracted from the one of the segmented portions utilizing the set of extraction rules and each extracted question stored in a crowd-sourced database of questions for CTDs.

Abstract: A method for crowdsourced answer selection for question-answer processing in automated commercial tender document (CTD) response generation includes populating a database with questions extracted from past CTDs and clustering the questions into groups of similar questions. Then, for each of the genus questions, a set of answers submitted in respectively different responses by multiple different responders are mapped to different ones of the past CTDs in connection with the genus question. Thereafter, the responses are rated and a present response document for a present CTD and also the present CTD are loaded into an editor. A question is extracted from the present CTD and the database queried with the extracted question. In response, a set of answers mapped to a genus question for the extracted question is retrieved and an answer in the set having a highest rating is inserted into the present response document for the extracted question.

Abstract: In a method for computer driven question identification and understanding within a commercial tender document (CTD), different CTDs from different individuals are uploaded into fixed storage of a computer. Then, different ones of the CTDs are loaded into memory and clustered into different clusters according to at least one clustering criteria. For each one of the CTDs, a corresponding one of the clusters is identified, and a segmentation model selected for the identified one of the clusters. Thereafter, segmentation is performed upon the CTD utilizing the selected segmentation model to produce a set of segmented portions of the CTD. Finally, for each one of the segmented portions, a set of extraction rules mapped to the one of the segmented portions is retrieved, at least one question extracted from the one of the segmented portions utilizing the set of extraction rules and each extracted question stored in a crowd-sourced database of questions for CTDs.

Abstract: A method for crowdsourced answer selection for question-answer processing in automated commercial tender document (CTD) response generation includes populating a database with questions extracted from past CTDs and clustering the questions into groups of similar questions. Then, for each of the genus questions, a set of answers submitted in respectively different responses by multiple different responders are mapped to different ones of the past CTDs in connection with the genus question. Thereafter, the responses are rated and a present response document for a present CTD and also the present CTD are loaded into an editor. A question is extracted from the present CTD and the database queried with the extracted question. In response, a set of answers mapped to a genus question for the extracted question is retrieved and an answer in the set having a highest rating is inserted into the present response document for the extracted question.